Rubin [1]} first introduced the idea of synthetic data as a confidentiality protection mechanism in 1993, proposing using multiple imputation on all variables such that none of the original data was released. In the same year, Little [2]} proposed an alternative that simulated only sensitive variables, thereby producing partially synthetic data. Rubin's idea was slow to be adopted, as noted by Raghunathan et al. [3]}, who along with Reiter [4]}, [5]}, [6]}, formalised the synthetic data problem. Further work (e.g. [7]}, [8]}, [9]}) has involved using non-parametric data synthesis methods such as classification and regression trees (CART) and random forests and more recently deep learning methods such as Generative Adversarial Networks [10]} have also been used to generate synthetic data.
